All right, full rockstar treatment for Alexander Wang, everyone. All right, so why don't we start out? Backstage we were saying, you know, one of the cool ways to think about this event is like, you know, this room is actually full of people who are just like us, but when we were 18 or 20 or, you know, there's some 16-year-olds in this audience, you know. Let's jump to your story. I mean, you got it, came up always really smart, like math olympiad, like jump us to, you know, the Alex of that time, like what were you feeling? What were you thinking? And what drove you down this road?
Yeah, I grew up in New Mexico, Los Alamos, New Mexico, which now is famous, but it really was the middle of nowhere. And I remember I did all these math competitions, all these computer science competitions, but then I knew I wanted to do really big things and it was like not exactly clear how or what the exact paths to do that would be. And I had a friend who was really into programming. And you know, after high school got an internship in the valley, worked, I think his first internship was at Palantir. And he was kind of this influence for me. And so, after I finished high school, I ended up working at Quora, here in Silicon Valley. And then I worked there for a year. I took a gap year to work there. And then I went to MIT. And this was, I was 19 when I worked at Quora. I was 18 when I went to MIT. And I was 19 when I started Scale. And I remember this period from like 17 to 19. It was, I felt like I was constantly changing, like you know exactly what I want to do was constantly changing, you know I was learning so much just from the people around me and it was just like I felt like I was drinking from the fire hose pretty constantly during that time. And I would definitely recommend, you know, the two things that were really important. One is I think working at a company was really valuable because like I think from the outside in you have no idea how companies work. You have no idea what it looks like to actually build something. You have no idea what it looks like to iterate on something. You have no idea what it looks like for groups of people to make decisions. And so I thought that was really important. And then going to school at MIT was actually really important because it just gave me a lot of opportunity to explore what was interesting. And so it was at MIT that I started training my first models and that I like played around with TensorFlow which had just come out that year at MIT and where I like ultimately came up with the idea of Scale. And then after one year of MIT, I applied to YC. You know, it felt like kind of like a miracle to get in at that time. And YC was really critical to my entrepreneurial journey. Like I don't think, like YC is this amazing blend of, you know, they're very supportive and they obviously want you to succeed, but they also give it to you very real and they tell you when you're being a dumbass. Which I think is, you know, that's what we all need in life. So yeah, that was I think the story till then I was 19 started Scale and the rest is history.
I guess with, you worked with Jared Friedman at the time and you came in with actually a very different idea than what ended up becoming Scale.
Yeah. Yeah. So, we wanted to build like an AI agent, funnily enough, for, for doc, for to help people like get medical care. And it was like the right, it was a great example of an idea that I think will ultimately exist. Like I think we're even seeing it now like AI agents to help people get medical care are very real, but it was the wrong timing. And we worked on it for about a month or two before Jared pulled us aside and we're like, "Guys, this is, I don't know if this is going to go anywhere." And that's exactly what we need to hear. And it was at that time when like, you know, I had studied AI at MIT. I like trained models and we thought we sort of went back to the drawing board, thought deeply about where the opportunity was and came up with Scale. I guess selling data at the time, you know, large language models were not even, had not really come to the fore yet. But self-driving cars were sort of coming up and computer vision suddenly became, so that was sort of the first market. Is that right?
Yeah. So, the story here is that like I was when I was at MIT, I did a bunch of projects to like train models of various forms and these were like, you know, by comparison today, they're like little toy models. And I remember to train a model I needed three things. I needed a GCP account like I need an account on some cloud service to get compute. I needed the code to run to actually train the model. And I needed data. I needed a data set. And for two out of these three things, you could just press a button online and get them. And then for the last one for data, there was like no effective way to get data for training these models. And so it felt incredibly obvious that this was going to be the future that there was going to be a way to, you know, press a button so to speak and get data. And it was very funny because in the years that followed like in the first many years of Scale data was very unsexy still. Every time we would go out to fundraise, even though our numbers were great and we had great revenue, you know, VCs and investors would always be very skeptical. They'd be like, "Oh, I don't know if this is a good business. Does this have longevity? Is this durable?" And it was really weird to me, but, you know, none of the investors had ever trained a model, so I guess they didn't really get it. And fast forward to today, you know, we managed to raise money, we managed to keep going, managed to keep growing the business, but the very same investors who passed on us and were very bearish on the potential of AI are writing think pieces today about how data is so critical and is one of the biggest business opportunities in AI. So, it's very funny to see that whole thing come full circle.
It seems like that's actually a real good case study in first principles thinking, right? Like you can't start a company by opening the pages of the Wall Street Journal and saying, "Well, this data is hot. Like we're going to go work on that." Like you literally couldn't have started Scale that way. You had to start from think like simple statements that are about the world that you know to be true and then sort of building something for that.
Yeah. I think the key thing is you need to develop conviction in a set of beliefs that nobody else agrees with. Like I think if you look at all the most successful companies in the world they were started at a time long before the sort of like core idea was popular and they work on that. They toil in obscurity for years and years before, you know, the idea or the space or the concept or the business, you know, becomes consensus. And the only way you're going to be successful is if you're able to identify these truths about the world early, long before everyone else. And I think that like I mean, one of the most surprising things like, you know, Scale, we've been working on AI for a decade. You know, you just you can't base your business decisions based on what everyone else is saying around you. Like if you go too much with the herd, you will get immensely confused and you will end up nowhere. And so you have to develop your own compass of what you think the future's going to look like because everyone else will just confuse you.
It seems like one of the things you got incredibly great at was, you know, you start with this kernel of like we believe X and nobody else believes it, but then the mechanics of building the business are talking to investors and convincing them and not letting them demoralize you, talking to customers who I mean should just get it and then especially like convincing people to come work for you.
Yeah. I think that these early mechanics of building a company like these are things that I think you might have some predisposition to be good at, but like nobody is good at starting a company when they start a company. And I remember talking to a lot of the investors who I met very early on and they, you know, a lot of them would say like, oh, like, you know, you just grew so quickly and you changed so quickly and like I didn't, you know, I didn't see it at the time. And I think that's probably true for literally everyone who starts a company. Like nobody is good at something they've never done before, right? And so I think for all entrepreneurs, you start out pretty shitty at everything. And the whole game is how do you develop yourself to continuously improve to get better and learn quickly?
Backstage we were talking about this is actually a really lucky time to start a company because you know obviously you can come do YC, you know the people in this room have each other which is kind of wild but not only that now you have ideally a personal AI that's going to tell you you know hey these are some ways to do it. Do you think that would have helped you like accelerate even faster? Like, you know, talk what do you think it's like to start a company today with AI in the age of AI?
Yeah, I mean, I really think we're at this like amazing moment in the world where the bottleneck is not the progress of the AI models. The bottleneck is diffusing that through the rest of the world and helping the world adapt to this amazing technology that already exists. Like I think if the models didn't improve at all from today, there would still be like decades and decades of like total upheaval and change in the economy and how the world operates and everything around us. And so I think it's as a result it's like one of the most incredible, it's probably a like once in a civilization opportunity to be a dreamer and to have a vision and to have ambition and to impose a view of how the future world should look by building something amazing. You know one of the things that we were chatting about, you know backstage is you know when I started Scale or you know 10 years ago if you start a company you had to be, you know it was like David versus Goliath and you had to be clever and you had to find like an angle into the market and you had to sort of like you know figure out a way to compete even though you had much fewer resources. And now I actually think with the power of agents and AI broadly speaking it's much closer to Goliath versus Goliath like I think but maybe the startup is like a mecha-Goliath that is like vastly enhanced by the power of agents and AI and you know the large companies are the sort of like more traditional Goliath so to speak. But I think that startups now like if you properly embrace AI agents and figure out the way to leverage their strengths in the most like ambitious ways, you can easily outcompete incumbents.
So let's talk about super intelligence because that's clearly that's even in the name of your lab. What does super intelligence mean operationally inside Meta right now?
Yeah, I think that you know we a year ago Mark wrote this memo about personal super intelligence which I think actually is very similar to your concept of personal AGI but you know we believe that everybody in the world, you know all the billions of people in the world are going to have a super intelligence that is adapted and tailored to them that enables them to accomplish their goals, knows their context and ultimately is an expander of their own agency. Like I think the thing that we think a lot about is agency expansion. How do we help people accomplish things that they couldn't have ever dreamed of before? And what would everyone in the world do if everything was just easy? And we think about this in an ecosystem way as well. I think you know Patrick mentioned it, but you know we don't believe in this totalizing, you know totalitarian view of you know AIs that control the world. We believe that these are going to enhance this very broad ecosystem. So you know we believe in billions of people all around the world all having their own personal super intelligence and we also believe in you know an explosion of entrepreneurship. There's 200 million businesses that are on Meta platforms today. We think that number should go to billions with this explosion of creativity and using AI tools. And ultimately we think that you know it's going to be this like dynamic ecosystem of business agents working with you know personal agents and developing this sort of like complex ecosystem that is fully AI supercharged.
So I was really psyched to see Meta Spark 1.1, my Open Claw absolutely loved it. How has running a frontier lab been? The Meta Spark level is sort of the Opus level. What's coming down the pipe and also I think that you're increasingly looking at open source which I think this audience really loves.
Yeah. Yeah. So I think it's been, you know I've been at Meta for about a year now and it's been quite a year. I think you know getting in and you know Meta we've talked about it publicly like Llama before wasn't on the trajectory that was needed for Meta and so I got in there and we kind of did a zero-based build of how do you you know build an entire frontier lab you know in some ways kind of from scratch obviously using a lot of what we had and move as quickly as possible and so within nine months of that moment we launched Llama 3 and then two months later we launched Llama 3.1 and Llama 3.1 405B and you know there's a few things that I think have really struck me about this. You know the first is talent density was incredibly important that was the core thing to bet on and like talent density is something that compounds naturally like the more talented people you have the more of the most talented people want to join you. You know and I think it's kind of amazing to see on the inside but you know frontier AI work is research like we are, it is scientific work we're exploring what can you do with these models how can you push these models what is the what are the reaches of what can be accomplished with these models which requires a totally different mindset and operating model than you know existed for internet companies or internet products and whatnot there's a lot more about experimentation about science about scaling. And everything ultimately is about how do you develop a lab, an operating model, a system that will just be able to compound with all of the exponential growth that will happen in the ecosystem. Both the exponential growth in capabilities, the exponential growth in compute, the exponential growth in adoption and usage. Like these are all, we are on this like very very steep exponent across maybe every dimension of the ecosystem. And it's important to develop like an organism. That's how you think about the lab that is able to sort of grow with that. No, it's been very exciting and we're going to be shipping a lot more. So, I think you know we just launched Llama 3.1 which was a great model. We're going to continue to have updates on the Llama 3 line. We'll also have bigger models on the way that I think will be much more competitive with even the very best models that are out there today. We're going to be launching a harness soon and have been working on a harness to help empower all the developers and agentic developers out there. And then we're also, you know as you mentioned we're working on open source models and we want to kind of as I described before like we believe in a decentralized world of AI capability and progress and development. We want to empower the broader ecosystem and everyone in the world to be able to build and develop using this technology. And so we have a lot of exciting things on the way and I think we want to empower the ecosystem and developers as much as humanly possible.
I mean it sounds like one of the ways I mean certainly when I was using Llama 3 with my Open Claw like it became clear that it was as good as Opus especially for that sort of agentic flow with skill files but it was like 8x cheaper actually.
Yes. Well we I think this goes to it like I don't you know we don't believe in a world where these models are so expensive that you know they get rationed only for the most wealthy of developers and companies. It's important for everyone to be able to use the technology and to build whatever they want to build with it. And I think that you know we take a view I think the best AI products haven't even been developed yet. You know that if you look at the AI ecosystem and everything that's happened like every wave is 10 times bigger than the past wave. So you know when I started Scale the first wave was maybe self-driving cars. Self-driving cars are really awesome. They're like really really cool. But that was like pales in comparison to large language models and chat bots. And like you know chat bots became this thing that was like probably 10 times bigger even than self-driving cars. And then there were coding agents which came a few years later. And coding agents are probably 10 times bigger than chat bots. And I think we're just on this steep curve like we're going to keep seeing these new modalities and form factors and developments of the AI paradigm that will each be dramatically bigger than the last. And so you know our point of view is like let's unleash the ecosystem. Let's explore and let's see, let's build you know kind of the future of the world together.
So what's the best way to actually take advantage of the coding model for Llama 3? It's open code, right?
Yeah. Today the easiest way is to use Open Code. We have like onboarding on the website and then soon we'll have a harness of our own and ultimately I think we want great models that plug into all of the available harnesses and empower as much you know sort of combinatorial innovation the ecosystem as possible.
Yeah, I know the harness is you know under wraps still, but like can you tease us with you know I mean I still use Open Claw, I still use Hermes agent. You know it's you know these things are I call them Ferraris that break down on the side of the road all the time. Like is this a Ferrari that won't break down? Like you know tease us a little bit.
Yeah. Hopefully it doesn't break down. I mean I think we're really focused on speed. I think speed is you know for anyone that uses these tools speed is probably the you know one of the most critical things. I think also reliability like you mentioned we want to be extremely reliable. We want to be very extensible and to scale to as complex and interesting of a multi-agent setup that you want to have like I think there's so much innovation that will occur even above the harness frankly in terms of like how to orchestrate and set up loops and develop like you know very complex ecosystems of these agents working together. We want to be really extensible and ultimately we want to just empower people to harness this technology because harness, no pun, actually pun not intended but there's like I truly believe these models are already just incredibly powerful like they should be so powerful to fuel you know many many points of expansion of GDP growth and I think it's like up to smart people with vision and ambition to make all that happen.
